Russian Journal of Earth Sciences Russian Journal of Earth Sciences Russian Journal of Earth Sciences 1681-1208 46516 10.2205/2020ES000707 ОРИГИНАЛЬНЫЕ СТАТЬИ ORIGINAL ARTICLES ОРИГИНАЛЬНЫЕ СТАТЬИ Methodology of physiography zoning using machine learning: A case study of the Black Sea Methodology of physiography zoning using machine learning: A case study of the Black Sea Krivoguz Denis Krivoguz Denis krivoguzdenis@gmail.com "Fisheries Oceanography" department, Research Institute of the Azov Sea Fishery Problems (AzNIIRKH) ru "Fisheries Oceanography" department, Research Institute of the Azov Sea Fishery Problems (AzNIIRKH) ru 20 2 1 10 29 10 2021 https://ras.editorum.ru/en/nauka/article/46516/view

Problem of area's zoning is very important and is one of the main problems of modern geographical science. Our point is to from a modern approach, based on the machine learning methods to provide zoning of any area. Key ideas of this methodology, that any distribution of factors that form any geographical system grouped around some clusters - unique zones that represents specific nature conditions. Formed methodology based on several stages - selection of data and objects for analysis, data normalization, assessment of predisposition of data for clustering, choosing the optimal number of clusters, clustering and validation of results. As an example, we tried to zone a surface layer of the Black Sea. We find that optimal number of unique zones is 3. Also, we find that the key driver of zone forming is a location of the rivers. Thus, we can say, that applying a machine learning approach in area's zoning tasks helps us increasing the quality of nature using and decision-making processes.

Problem of area's zoning is very important and is one of the main problems of modern geographical science. Our point is to from a modern approach, based on the machine learning methods to provide zoning of any area. Key ideas of this methodology, that any distribution of factors that form any geographical system grouped around some clusters - unique zones that represents specific nature conditions. Formed methodology based on several stages - selection of data and objects for analysis, data normalization, assessment of predisposition of data for clustering, choosing the optimal number of clusters, clustering and validation of results. As an example, we tried to zone a surface layer of the Black Sea. We find that optimal number of unique zones is 3. Also, we find that the key driver of zone forming is a location of the rivers. Thus, we can say, that applying a machine learning approach in area's zoning tasks helps us increasing the quality of nature using and decision-making processes.

 Spatial zoning Machine learning 𝑘-means clustering Black Sea Physiography zoning GIS clustering methodology Spatial zoning Machine learning 𝑘-means clustering Black Sea Physiography zoning GIS clustering methodology

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